Heritability of metabolic phenotypes in a Brazilian population: type 2 diabetes mellitus as application model

O desenvolvimento de formas comuns de diabetes ocorre a partir da interação de fatores ambientais e genéticos. Como consequências da falta de controle glicêmico nesses pacientes várias complicações são geradas. Estudos metabolômicos para diabetes mellitus tipo 2 no soro/plasma relataram mudanças em vários metabólitos, os quais podem ser considerados possíveis alvos para futuras pesquisas mecanicistas. Como o diabetes mellitus tipo 2 é uma doença que altera o perfil metabólico em vários níveis, este trabalho teve como objetivo comparar os indivíduos com diabetes mellitus tipo 2 e com indivíduos não-diabéticos. Além disso, foram explorados o design exclusivo de um estudo de base familiar para trazer uma melhor compreensão da relação causal de metabólitos identificados e o diabetes. No presente estudo, metabolômica de base populacional foi realizada em 939 amostras de soro de indivíduos participantes do Projeto Corações de Baependi. Os participantes foram separados em dois grupos: diabéticos (77 indivíduos) e não-diabéticos (862 indivíduos). Com a técnica de GC/MS, normalização e análise estatística utilizadas, foi possível identificar metabólitos diferencialmente alterados em soro de diabéticos e não-diabéticos. Foram identificados 72 metabólitos com diferentes concentrações médias em indivíduos com diabetes mellitus tipo 2, em comparação com indivíduos saudáveis. Foi possível recapitular as principais vias que são alteradas no indivíduo diabético e a identificação de metabólitos sugestivos de estarem elevados no diabetes. Os dados de hereditariedade puderam ser utilizados para uma melhor compreensão da relação causal das associações observadas e ajudar a priorizar metabólitos associados ao diabetes para trabalhos futurosThe development of common forms of diabetes comes from the interaction between environmental and genetic factors, and the consequences of poor glycemic control in these patients can result in several complications. Metabolomics studies for type 2 diabetes mellitus in serum/plasma have reported changes in numerous metabolites, which might be considered possible targets for future mechanistic research. As type 2 diabetes is a disease that changes the metabolic profile in several levels, this work aimed to compare type 2 diabetes mellitus and non-diabetic participants of a family-based epidemiological study. In addition, we exploited the unique design of a family-based study to bring a better understanding of the causal relationship of identified metabolites and diabetes. In the current study, population based metabolomics was applied in 939 participants of \"the Baependi Heart Study\". Participants were separated into two groups: diabetic (77 individuals) and non-diabetic (862 individuals). By GC/MS technique, normalization and statistical analysis, it was possible to identify differentially concentrated metabolites in serum of diabetics and non-diabetics. 72 metabolites were identified as up regulated in type 2 diabetes mellitus subjects compared to non-diabetic individuals. It was possible to recapitulate the main pathways that are changed in the diabetic subject and the identification of metabolites suggestive of being upstream of diabetes. Heritability data was used to derive a better understanding of the causal relationship of the observed associations and help to prioritize diabetes-associated metabolites for further wor

Metabolomics biomarkers and the risk of overall mortality and ESRD in CKD: Results from the Progredir Cohort.

INTRODUCTION:Studies on metabolomics and CKD have primarily addressed CKD incidence defined as a decline on eGFR or appearance of albuminuria in the general population, with very few evaluating hard outcomes. In the present study, we investigated the association between metabolites and mortality and ESRD in a CKD cohort. SETTING AND METHODS:Data on 454 participants of the Progredir Cohort Study, Sao Paulo, Brazil were used. Metabolomics was performed by GC-MS (Agilent MassHunter) and metabolites were identified using Agilent Fiehn GC/MS and NIST libraries. After excluding metabolites present in <50% of participants, 293 metabolites were analyzed. An FDR q value <0.05 criteria was applied in Cox models on the composite outcome (mortality or incident renal replacement therapy) adjusted for batch effect, resulting in 34 metabolites associated with the outcome. Multivariable-adjusted Cox models were then built for the composite outcome, death, and ESRD incident events. Competing risk analysis was also performed for ESRD. RESULTS:Mean age was 68±12y, mean eGFR-CKDEPI was 38.4±14.6 ml/min/1.73m2 and 57% were diabetic. After adjustments (GC-MS batch, sex, age, DM and eGFR), 18 metabolites remained significantly associated with the composite outcome. Nine metabolites were independently associated with death: D-malic acid (HR 1.84, 95%CI 1.32-2.56, p = 0.0003), acetohydroxamic acid (HR 1.90, 95%CI 1.30-2.78, p = 0.0008), butanoic acid (HR 1.59, 95%CI 1.17-2.15, p = 0.003), and docosahexaenoic acid (HR 0.58, 95%CI 0.39-0.88, p = 0.009), among the top associations. Lactose (SHR 1.49, 95%CI 1.04-2.12, p = 0.03), 2-O-glycerol-α-D-galactopyranoside (SHR 1.76, 95%CI 1.06-2.92, p = 0.03), and tyrosine (SHR 0.52, 95%CI 0.31-0.88, p = 0.02) were associated to ESRD risk, while D-threitol, mannitol and myo-inositol presented strong borderline associations. CONCLUSION:Our results identify specific metabolites related to hard outcomes in a CKD population. These findings point to the need of further exploration of these metabolites as biomarkers in CKD and the understanding of the underlying biological mechanisms related to the observed associations

Genetic Studies of Hypertrophic Cardiomyopathy in Singaporeans Identify Variants in TNNI3 and TNNT2 That Are Common in Chinese Patients

10.1161/CIRCGEN.119.002823CIRCULATION-GENOMIC AND PRECISION MEDICINE135424-43